When you ask Siri a question or request a song through Alexa, you're using automated speech recognition software. Companies use AI services to screen job applicants. Court reporters use speech recognition tools to produce records of depositions and trial proceedings. Physicians use software by Nuance and Suki to dictate clinical notes. If you have a physical impairment, you might use speech recognition software to navigate a web browser. YouTube uses it to create automatic captions, whose malaprops inspired a parody series called Caption Fail.

Facebook in January agreed to a historic $550 million settlement over its face-identifying technology. But now, the federal judge overseeing the case is refusing the accept the deal. Facebook in January agreed to a historic $550 million settlement over its face-identifying technology. But now, the federal judge overseeing the case is refusing the accept the deal. Next week, lawyers for Facebook will be back in court, trying to convince a judge they should be allowed to settle a class action suit that accuses the company of violating users' privacy.

A personal assistant arrested Friday in the death and dismemberment of a 33-year-old tech entrepreneur was believed to have owed his boss a "significant amount of money," New York City police said. Tyrese Haspil faces a murder charge in the death of Fahim Saleh, whose beheaded, armless body was found Tuesday by a cousin who had gone to his luxury Manhattan condo to check on him. Haspil, 21, handled finances and personal matters for Saleh, whose ventures included a ride-hailing service in Africa. Haspil, who grew up on Long Island and lived in Brooklyn, was taken into police custody Friday morning in the lobby of another luxury apartment building several blocks from where Saleh was killed, NYPD Chief of Detectives Rodney Harrison said. Information on Haspil's arraignment or a lawyer who could speak on his behalf was not immediately available.

The unprecedented progress in Artificial Intelligence (AI) [1-6], over the last decade, came alongside of multiple AI failures [7, 8] and cases of dual use [9] causing a realization [10] that it is not sufficient to create highly capable machines, but that it is even more important to make sure that intelligent machines are beneficial [11] for the humanity. This lead to the birth of the new subfield of research commonly known as AI Safety and Security [12] with hundreds of papers and books published annually on different aspects of the problem [13-31]. All such research is done under the assumption that the problem of controlling highly capable intelligent machines is solvable, which has not been established by any rigorous means. However, it is a standard practice in computer science to first show that a problem doesn't belong to a class of unsolvable problems [32, 33] before investing resources into trying to solve it or deciding what approaches to try. Unfortunately, to the best of our knowledge no mathematical proof or even rigorous argumentation has been published demonstrating that the AI control problem may be solvable, even in principle, much less in practice. Or as Gans puts it citing Bostrom: "Thusfar, AI researchers and philosophers have not been able to come up with methods of control that would ensure [bad] outcomes did not take place …" [34].

We propose a distributionally robust logistic regression model with an unfairness penalty that prevents discrimination with respect to sensitive attributes such as gender or ethnicity. This model is equivalent to a tractable convex optimization problem if a Wasserstein ball centered at the empirical distribution on the training data is used to model distributional uncertainty and if a new convex unfairness measure is used to incentivize equalized opportunities. We demonstrate that the resulting classifier improves fairness at a marginal loss of predictive accuracy on both synthetic and real datasets. We also derive linear programming-based confidence bounds on the level of unfairness of any pre-trained classifier by leveraging techniques from optimal uncertainty quantification over Wasserstein balls.

Now here's a crazy idea: What if the data we all generate on a day to day basis benefited us, instead of the companies that collect it? It may sound nuts at first, but some AI experts see a future in which people hold full control over their data and smart digital assistants infused with AI work to protect and monetize a person's individual's data for his or her benefit. This vision of a more equitable big data world is one that's held by Sri Ambati. The H2O.ai founder and CEO sees a day not too far in the future in which people are empowered to control their own data as an asset, and even to profit directly from their data, which is something that only a handful of individuals are currently able to do. "Today, whether we want it or not, our data is stored on giant social networks," Ambati tells Datanami.

Yeshimabeit Milner was in high school the first time she saw kids she knew getting handcuffed and stuffed into police cars. It was February 29, 2008, and the principal of a nearby school in Miami, with a majority Haitian and African-American population, had put one of his students in a chokehold. The next day several dozen kids staged a peaceful demonstration. That night, Miami's NBC 6 News at Six kicked off with a segment called "Chaos on Campus." Cut to blurry phone footage of screaming teenagers: "The chaos you see is an all-out brawl inside the school's cafeteria." Students told reporters that police hit them with batons, threw them on the floor, and pushed them up against walls. The police claimed they were the ones getting attacked--"with water bottles, soda pops, milk, and so on"--and called for emergency backup. Around 25 students were arrested, and many were charged with multiple crimes, including resisting arrest with violence.

Ahead of the latest episode in the Boyes Turner tech podcast series, Prof J.Mark Bishop shares his thoughts on'e-Discovery and Artificial Intelligence... Events unfold and you are dropped into the opening of a long and complex case with 500,000 emails to sift through and you're not even sure what you are looking for, who you are looking for, or when any incidents of interest may have occurred. Currently the review of documents is the most labour-intensive task of an e-discovery investigation often consuming more than 75% of the project budget. This is largely because researchers review the documents manually. To put this into context, to review half a million documents by hand, at 25 documents an hour, would take around 20,000 person-hours. Hence, because it is practically impossible to review all documents in the target corpus by hand, results are too often limited by simple keyword searches. Unfortunately coming up with responsive keywords is not trivial as a researcher often does not know exactly what she is looking for beforehand.

Facial recognition software has been the subject of debate for a long time now. Despite the controversy, law enforcement has been using such AI-powered software to catch criminals all around the world, and most particularly in large nations with less strict privacy laws. The use is prevalent despite the fact that the software may not work accurately when used on ethnic communities, youngsters and even women. One company which is leading the news headlines these days is Clearview AI founded by an Australian entrepreneur Hoan Ton-That. Although Clearview AI hasn't devised a groundbreaking facial recognition app, what it sells can be deemed as useful to law enforcement agencies.

Concerns about the societal impact of AI-based services and systems has encouraged governments and other organisations around the world to propose AI policy frameworks to address fairness, accountability, transparency and related topics. To achieve the objectives of these frameworks, the data and software engineers who build machine-learning systems require knowledge about a variety of relevant supporting tools and techniques. In this paper we provide an overview of technologies that support building trustworthy machine learning systems, i.e., systems whose properties justify that people place trust in them. We argue that four categories of system properties are instrumental in achieving the policy objectives, namely fairness, explainability, auditability and safety & security (FEAS). We discuss how these properties need to be considered across all stages of the machine learning life cycle, from data collection through run-time model inference. As a consequence, we survey in this paper the main technologies with respect to all four of the FEAS properties, for data-centric as well as model-centric stages of the machine learning system life cycle. We conclude with an identification of open research problems, with a particular focus on the connection between trustworthy machine learning technologies and their implications for individuals and society.